Efficient entropy-based features selection for image retrieval
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Perceptual color descriptor based on spatial distribution: A top-down approach
Image and Vision Computing
An effective image retrieval scheme using color, texture and shape features
Computer Standards & Interfaces
Color and texture features for content based image retrieval system
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
A compact auto color correlation using binary coding stream for image retrieval
Proceedings of the 15th WSEAS international conference on Computers
Texton theory revisited: A bag-of-words approach to combine textons
Pattern Recognition
Multifeature analysis and semantic context learning for image classification
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
A new content-based image retrieval technique using color and texture information
Computers and Electrical Engineering
Content-based image retrieval using OWA fuzzy linking histogram
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Computational intelligence models for image processing and information reasoning
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In this paper, we propose a content-based image retrieval method based on an efficient combination of multiresolution color and texture features. As its color features, color autocorrelo- grams of the hue and saturation component images in HSV color space are used. As its texture features, BDIP and BVLC moments of the value component image are adopted. The color and texture features are extracted in multiresolution wavelet domain and combined. The dimension of the combined feature vector is determined at a point where the retrieval accuracy becomes saturated. Experimental results show that the proposed method yields higher retrieval accuracy than some conventional methods even though its feature vector dimension is not higher than those of the latter for six test DBs. Especially, it demonstrates more excellent retrieval accuracy for queries and target images of various resolutions. In addition, the proposed method almost always shows performance gain in precision versus recall and in ANMRR over the other methods.